Description: The mission of Technometrics is to contribute to the development and use of statistical methods in the physical, chemical, and engineering sciences. Its content features papers that describe new statistical techniques, illustrate innovative application of known statistical methods, or review methods, issues, or philosophy in a particular area of statistics or science, when such papers are consistent with the journal's mission. Application of proposed methodology is justified, usually by means of an actual problem in the physical, chemical, or engineering sciences.

Papers in the journal reflect modern practice. This includes an emphasis on new statistical approaches to screening, modeling, pattern characterization, and change detection that take advantage of massive computing capabilities. Papers also reflect shifts in attitudes about data analysis (e.g., less formal hypothesis testing, more fitted models via graphical analysis), and in how important application areas are managed (e.g., quality assurance through robust design rather than detailed inspection).

The "moving wall" represents the time period between the last issue
available in JSTOR and the most recently published issue of a journal.
Moving walls are generally represented in years. In rare instances, a
publisher has elected to have a "zero" moving wall, so their current
issues are available in JSTOR shortly after publication.
Note: In calculating the moving wall, the current year is not counted.
For example, if the current year is 2008 and a journal has a 5 year
moving wall, articles from the year 2002 are available.

Terms Related to the Moving Wall

Fixed walls: Journals with no new volumes being added to the archive.

Absorbed: Journals that are combined with another title.

Complete: Journals that are no longer published or that have been
combined with another title.

Abstract

Ridge analysis is a graphical and inferential method for exploring optimum factor levels of a response surface at fixed distances from the center of the experimental region. This article proposes an approach to doing a ridge analysis for optimizing a response surface in the presence of noise variables. We extend the ridge analysis method of Peterson to include some of the factors as noise variables. This approach allows an investigator to explore factor combinations that lower the mean squared error about a target value, while at the same time keeping track of how much the mean response differs from the target value. It also allows an investigator to compute a simultaneous confidence band about the root mean squared error about a target value. This provides a guidance band to aid in determining optimal levels of operation. A variety of factor constraints can be imposed, including those found in mixture experiments. In addition, we propose a modification of our approach that can be used for "larger is better" or "smaller is better" experiments. We illustrate the proposed method using two examples, one of which is a mixture experiment.